Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment

Abstract An effective urban drainage system (UDS) is crucial for solving urban flooding problems, motivating plenty of studies to design, build and rehabilitate UDSs. However, the existing design and analysis methods usually assume a uniformly spatial distribution of rainfall intensity throughout an...
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Autor*in:	Lin, Ruozhou [verfasserIn] Zheng, Feifei Ma, Yiyi Duan, Huan-Feng Chu, Shipeng Deng, Zhengzhi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Spatial variation of rainfall intensity (SVRI) Urban flooding Urban drainage system (UDS) Uncertainty

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Water resources management - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 36(2022), 14 vom: 19. Sept., Seite 5655-5673
Übergeordnetes Werk:	volume:36 ; year:2022 ; number:14 ; day:19 ; month:09 ; pages:5655-5673

Links:	Volltext

DOI / URN:	10.1007/s11269-022-03325-8

Katalog-ID:	SPR04845012X

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520			\|a Abstract An effective urban drainage system (UDS) is crucial for solving urban flooding problems, motivating plenty of studies to design, build and rehabilitate UDSs. However, the existing design and analysis methods usually assume a uniformly spatial distribution of rainfall intensity throughout an urban catchment, while there is an observably spatial variation of rainfall intensity (SVRI) in most practical systems, especially for short-duration storms and/or large-scale catchments. The assumption ignoring SVRI might fully or partially underestimate the runoffs locally and thus increase the partial flooding risks for the UDS designed under uniformly spatial rainfall distribution. To address this issue, this paper proposes an improved framework with two spatially variable rainfall models (SVRMs) to evaluate the impacts of SVRI on urban flooding. In this proposed framework, four aspects of improvements have been implemented: (i) both SVRMs are derived from the spatially uniform hyetographs to ensure the same total precipitation volume; (ii) both SVRMs utilize the density function of truncated two-dimensional Gaussian distribution to approximate the pattern of SVRI; (iii) different characteristics of SVRI are quantified in these two SVRMs respectively, and (iv) the Monte Carlo method is adopted to implement the uncertainty of rainfall intensity in SVRMs. Besides, two real-world UDSs of different configurations and scales are used to demonstrate the effectiveness of the developed framework. The application results show that the SVRI could significantly aggravate urban flooding risk including flooding duration and volume, and the impact patterns may vary with the characteristics of UDSs. The results and findings of this study also indicate the importance of taking SVRI into consideration in UDS design and flooding assessment practice.
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allfields	10.1007/s11269-022-03325-8 doi (DE-627)SPR04845012X (SPR)s11269-022-03325-8-e DE-627 ger DE-627 rakwb eng Lin, Ruozhou verfasserin aut Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract An effective urban drainage system (UDS) is crucial for solving urban flooding problems, motivating plenty of studies to design, build and rehabilitate UDSs. However, the existing design and analysis methods usually assume a uniformly spatial distribution of rainfall intensity throughout an urban catchment, while there is an observably spatial variation of rainfall intensity (SVRI) in most practical systems, especially for short-duration storms and/or large-scale catchments. The assumption ignoring SVRI might fully or partially underestimate the runoffs locally and thus increase the partial flooding risks for the UDS designed under uniformly spatial rainfall distribution. To address this issue, this paper proposes an improved framework with two spatially variable rainfall models (SVRMs) to evaluate the impacts of SVRI on urban flooding. In this proposed framework, four aspects of improvements have been implemented: (i) both SVRMs are derived from the spatially uniform hyetographs to ensure the same total precipitation volume; (ii) both SVRMs utilize the density function of truncated two-dimensional Gaussian distribution to approximate the pattern of SVRI; (iii) different characteristics of SVRI are quantified in these two SVRMs respectively, and (iv) the Monte Carlo method is adopted to implement the uncertainty of rainfall intensity in SVRMs. Besides, two real-world UDSs of different configurations and scales are used to demonstrate the effectiveness of the developed framework. The application results show that the SVRI could significantly aggravate urban flooding risk including flooding duration and volume, and the impact patterns may vary with the characteristics of UDSs. The results and findings of this study also indicate the importance of taking SVRI into consideration in UDS design and flooding assessment practice. Spatial variation of rainfall intensity (SVRI) (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Urban drainage system (UDS) (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Zheng, Feifei (orcid)0000-0003-3048-7086 aut Ma, Yiyi aut Duan, Huan-Feng aut Chu, Shipeng aut Deng, Zhengzhi aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 36(2022), 14 vom: 19. Sept., Seite 5655-5673 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:36 year:2022 number:14 day:19 month:09 pages:5655-5673 https://dx.doi.org/10.1007/s11269-022-03325-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2022 14 19 09 5655-5673
spelling	10.1007/s11269-022-03325-8 doi (DE-627)SPR04845012X (SPR)s11269-022-03325-8-e DE-627 ger DE-627 rakwb eng Lin, Ruozhou verfasserin aut Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract An effective urban drainage system (UDS) is crucial for solving urban flooding problems, motivating plenty of studies to design, build and rehabilitate UDSs. However, the existing design and analysis methods usually assume a uniformly spatial distribution of rainfall intensity throughout an urban catchment, while there is an observably spatial variation of rainfall intensity (SVRI) in most practical systems, especially for short-duration storms and/or large-scale catchments. The assumption ignoring SVRI might fully or partially underestimate the runoffs locally and thus increase the partial flooding risks for the UDS designed under uniformly spatial rainfall distribution. To address this issue, this paper proposes an improved framework with two spatially variable rainfall models (SVRMs) to evaluate the impacts of SVRI on urban flooding. In this proposed framework, four aspects of improvements have been implemented: (i) both SVRMs are derived from the spatially uniform hyetographs to ensure the same total precipitation volume; (ii) both SVRMs utilize the density function of truncated two-dimensional Gaussian distribution to approximate the pattern of SVRI; (iii) different characteristics of SVRI are quantified in these two SVRMs respectively, and (iv) the Monte Carlo method is adopted to implement the uncertainty of rainfall intensity in SVRMs. Besides, two real-world UDSs of different configurations and scales are used to demonstrate the effectiveness of the developed framework. The application results show that the SVRI could significantly aggravate urban flooding risk including flooding duration and volume, and the impact patterns may vary with the characteristics of UDSs. The results and findings of this study also indicate the importance of taking SVRI into consideration in UDS design and flooding assessment practice. Spatial variation of rainfall intensity (SVRI) (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Urban drainage system (UDS) (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Zheng, Feifei (orcid)0000-0003-3048-7086 aut Ma, Yiyi aut Duan, Huan-Feng aut Chu, Shipeng aut Deng, Zhengzhi aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 36(2022), 14 vom: 19. Sept., Seite 5655-5673 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:36 year:2022 number:14 day:19 month:09 pages:5655-5673 https://dx.doi.org/10.1007/s11269-022-03325-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2022 14 19 09 5655-5673
allfields_unstemmed	10.1007/s11269-022-03325-8 doi (DE-627)SPR04845012X (SPR)s11269-022-03325-8-e DE-627 ger DE-627 rakwb eng Lin, Ruozhou verfasserin aut Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract An effective urban drainage system (UDS) is crucial for solving urban flooding problems, motivating plenty of studies to design, build and rehabilitate UDSs. However, the existing design and analysis methods usually assume a uniformly spatial distribution of rainfall intensity throughout an urban catchment, while there is an observably spatial variation of rainfall intensity (SVRI) in most practical systems, especially for short-duration storms and/or large-scale catchments. The assumption ignoring SVRI might fully or partially underestimate the runoffs locally and thus increase the partial flooding risks for the UDS designed under uniformly spatial rainfall distribution. To address this issue, this paper proposes an improved framework with two spatially variable rainfall models (SVRMs) to evaluate the impacts of SVRI on urban flooding. In this proposed framework, four aspects of improvements have been implemented: (i) both SVRMs are derived from the spatially uniform hyetographs to ensure the same total precipitation volume; (ii) both SVRMs utilize the density function of truncated two-dimensional Gaussian distribution to approximate the pattern of SVRI; (iii) different characteristics of SVRI are quantified in these two SVRMs respectively, and (iv) the Monte Carlo method is adopted to implement the uncertainty of rainfall intensity in SVRMs. Besides, two real-world UDSs of different configurations and scales are used to demonstrate the effectiveness of the developed framework. The application results show that the SVRI could significantly aggravate urban flooding risk including flooding duration and volume, and the impact patterns may vary with the characteristics of UDSs. The results and findings of this study also indicate the importance of taking SVRI into consideration in UDS design and flooding assessment practice. Spatial variation of rainfall intensity (SVRI) (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Urban drainage system (UDS) (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Zheng, Feifei (orcid)0000-0003-3048-7086 aut Ma, Yiyi aut Duan, Huan-Feng aut Chu, Shipeng aut Deng, Zhengzhi aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 36(2022), 14 vom: 19. Sept., Seite 5655-5673 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:36 year:2022 number:14 day:19 month:09 pages:5655-5673 https://dx.doi.org/10.1007/s11269-022-03325-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2022 14 19 09 5655-5673
allfieldsGer	10.1007/s11269-022-03325-8 doi (DE-627)SPR04845012X (SPR)s11269-022-03325-8-e DE-627 ger DE-627 rakwb eng Lin, Ruozhou verfasserin aut Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract An effective urban drainage system (UDS) is crucial for solving urban flooding problems, motivating plenty of studies to design, build and rehabilitate UDSs. However, the existing design and analysis methods usually assume a uniformly spatial distribution of rainfall intensity throughout an urban catchment, while there is an observably spatial variation of rainfall intensity (SVRI) in most practical systems, especially for short-duration storms and/or large-scale catchments. The assumption ignoring SVRI might fully or partially underestimate the runoffs locally and thus increase the partial flooding risks for the UDS designed under uniformly spatial rainfall distribution. To address this issue, this paper proposes an improved framework with two spatially variable rainfall models (SVRMs) to evaluate the impacts of SVRI on urban flooding. In this proposed framework, four aspects of improvements have been implemented: (i) both SVRMs are derived from the spatially uniform hyetographs to ensure the same total precipitation volume; (ii) both SVRMs utilize the density function of truncated two-dimensional Gaussian distribution to approximate the pattern of SVRI; (iii) different characteristics of SVRI are quantified in these two SVRMs respectively, and (iv) the Monte Carlo method is adopted to implement the uncertainty of rainfall intensity in SVRMs. Besides, two real-world UDSs of different configurations and scales are used to demonstrate the effectiveness of the developed framework. The application results show that the SVRI could significantly aggravate urban flooding risk including flooding duration and volume, and the impact patterns may vary with the characteristics of UDSs. The results and findings of this study also indicate the importance of taking SVRI into consideration in UDS design and flooding assessment practice. Spatial variation of rainfall intensity (SVRI) (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Urban drainage system (UDS) (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Zheng, Feifei (orcid)0000-0003-3048-7086 aut Ma, Yiyi aut Duan, Huan-Feng aut Chu, Shipeng aut Deng, Zhengzhi aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 36(2022), 14 vom: 19. Sept., Seite 5655-5673 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:36 year:2022 number:14 day:19 month:09 pages:5655-5673 https://dx.doi.org/10.1007/s11269-022-03325-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2022 14 19 09 5655-5673
allfieldsSound	10.1007/s11269-022-03325-8 doi (DE-627)SPR04845012X (SPR)s11269-022-03325-8-e DE-627 ger DE-627 rakwb eng Lin, Ruozhou verfasserin aut Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract An effective urban drainage system (UDS) is crucial for solving urban flooding problems, motivating plenty of studies to design, build and rehabilitate UDSs. However, the existing design and analysis methods usually assume a uniformly spatial distribution of rainfall intensity throughout an urban catchment, while there is an observably spatial variation of rainfall intensity (SVRI) in most practical systems, especially for short-duration storms and/or large-scale catchments. The assumption ignoring SVRI might fully or partially underestimate the runoffs locally and thus increase the partial flooding risks for the UDS designed under uniformly spatial rainfall distribution. To address this issue, this paper proposes an improved framework with two spatially variable rainfall models (SVRMs) to evaluate the impacts of SVRI on urban flooding. In this proposed framework, four aspects of improvements have been implemented: (i) both SVRMs are derived from the spatially uniform hyetographs to ensure the same total precipitation volume; (ii) both SVRMs utilize the density function of truncated two-dimensional Gaussian distribution to approximate the pattern of SVRI; (iii) different characteristics of SVRI are quantified in these two SVRMs respectively, and (iv) the Monte Carlo method is adopted to implement the uncertainty of rainfall intensity in SVRMs. Besides, two real-world UDSs of different configurations and scales are used to demonstrate the effectiveness of the developed framework. The application results show that the SVRI could significantly aggravate urban flooding risk including flooding duration and volume, and the impact patterns may vary with the characteristics of UDSs. The results and findings of this study also indicate the importance of taking SVRI into consideration in UDS design and flooding assessment practice. Spatial variation of rainfall intensity (SVRI) (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Urban drainage system (UDS) (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Zheng, Feifei (orcid)0000-0003-3048-7086 aut Ma, Yiyi aut Duan, Huan-Feng aut Chu, Shipeng aut Deng, Zhengzhi aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 36(2022), 14 vom: 19. Sept., Seite 5655-5673 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:36 year:2022 number:14 day:19 month:09 pages:5655-5673 https://dx.doi.org/10.1007/s11269-022-03325-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2022 14 19 09 5655-5673
language	English
source	Enthalten in Water resources management 36(2022), 14 vom: 19. Sept., Seite 5655-5673 volume:36 year:2022 number:14 day:19 month:09 pages:5655-5673
sourceStr	Enthalten in Water resources management 36(2022), 14 vom: 19. Sept., Seite 5655-5673 volume:36 year:2022 number:14 day:19 month:09 pages:5655-5673
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Spatial variation of rainfall intensity (SVRI) Urban flooding Urban drainage system (UDS) Uncertainty
isfreeaccess_bool	false
container_title	Water resources management
authorswithroles_txt_mv	Lin, Ruozhou @@aut@@ Zheng, Feifei @@aut@@ Ma, Yiyi @@aut@@ Duan, Huan-Feng @@aut@@ Chu, Shipeng @@aut@@ Deng, Zhengzhi @@aut@@
publishDateDaySort_date	2022-09-19T00:00:00Z
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author	Lin, Ruozhou
spellingShingle	Lin, Ruozhou misc Spatial variation of rainfall intensity (SVRI) misc Urban flooding misc Urban drainage system (UDS) misc Uncertainty Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment
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topic_title	Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment Spatial variation of rainfall intensity (SVRI) (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Urban drainage system (UDS) (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213
topic	misc Spatial variation of rainfall intensity (SVRI) misc Urban flooding misc Urban drainage system (UDS) misc Uncertainty
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title	Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment
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author_browse	Lin, Ruozhou Zheng, Feifei Ma, Yiyi Duan, Huan-Feng Chu, Shipeng Deng, Zhengzhi
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title_sort	impact of spatial variation and uncertainty of rainfall intensity on urban flooding assessment
title_auth	Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment
abstract	Abstract An effective urban drainage system (UDS) is crucial for solving urban flooding problems, motivating plenty of studies to design, build and rehabilitate UDSs. However, the existing design and analysis methods usually assume a uniformly spatial distribution of rainfall intensity throughout an urban catchment, while there is an observably spatial variation of rainfall intensity (SVRI) in most practical systems, especially for short-duration storms and/or large-scale catchments. The assumption ignoring SVRI might fully or partially underestimate the runoffs locally and thus increase the partial flooding risks for the UDS designed under uniformly spatial rainfall distribution. To address this issue, this paper proposes an improved framework with two spatially variable rainfall models (SVRMs) to evaluate the impacts of SVRI on urban flooding. In this proposed framework, four aspects of improvements have been implemented: (i) both SVRMs are derived from the spatially uniform hyetographs to ensure the same total precipitation volume; (ii) both SVRMs utilize the density function of truncated two-dimensional Gaussian distribution to approximate the pattern of SVRI; (iii) different characteristics of SVRI are quantified in these two SVRMs respectively, and (iv) the Monte Carlo method is adopted to implement the uncertainty of rainfall intensity in SVRMs. Besides, two real-world UDSs of different configurations and scales are used to demonstrate the effectiveness of the developed framework. The application results show that the SVRI could significantly aggravate urban flooding risk including flooding duration and volume, and the impact patterns may vary with the characteristics of UDSs. The results and findings of this study also indicate the importance of taking SVRI into consideration in UDS design and flooding assessment practice. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract An effective urban drainage system (UDS) is crucial for solving urban flooding problems, motivating plenty of studies to design, build and rehabilitate UDSs. However, the existing design and analysis methods usually assume a uniformly spatial distribution of rainfall intensity throughout an urban catchment, while there is an observably spatial variation of rainfall intensity (SVRI) in most practical systems, especially for short-duration storms and/or large-scale catchments. The assumption ignoring SVRI might fully or partially underestimate the runoffs locally and thus increase the partial flooding risks for the UDS designed under uniformly spatial rainfall distribution. To address this issue, this paper proposes an improved framework with two spatially variable rainfall models (SVRMs) to evaluate the impacts of SVRI on urban flooding. In this proposed framework, four aspects of improvements have been implemented: (i) both SVRMs are derived from the spatially uniform hyetographs to ensure the same total precipitation volume; (ii) both SVRMs utilize the density function of truncated two-dimensional Gaussian distribution to approximate the pattern of SVRI; (iii) different characteristics of SVRI are quantified in these two SVRMs respectively, and (iv) the Monte Carlo method is adopted to implement the uncertainty of rainfall intensity in SVRMs. Besides, two real-world UDSs of different configurations and scales are used to demonstrate the effectiveness of the developed framework. The application results show that the SVRI could significantly aggravate urban flooding risk including flooding duration and volume, and the impact patterns may vary with the characteristics of UDSs. The results and findings of this study also indicate the importance of taking SVRI into consideration in UDS design and flooding assessment practice. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract An effective urban drainage system (UDS) is crucial for solving urban flooding problems, motivating plenty of studies to design, build and rehabilitate UDSs. However, the existing design and analysis methods usually assume a uniformly spatial distribution of rainfall intensity throughout an urban catchment, while there is an observably spatial variation of rainfall intensity (SVRI) in most practical systems, especially for short-duration storms and/or large-scale catchments. The assumption ignoring SVRI might fully or partially underestimate the runoffs locally and thus increase the partial flooding risks for the UDS designed under uniformly spatial rainfall distribution. To address this issue, this paper proposes an improved framework with two spatially variable rainfall models (SVRMs) to evaluate the impacts of SVRI on urban flooding. In this proposed framework, four aspects of improvements have been implemented: (i) both SVRMs are derived from the spatially uniform hyetographs to ensure the same total precipitation volume; (ii) both SVRMs utilize the density function of truncated two-dimensional Gaussian distribution to approximate the pattern of SVRI; (iii) different characteristics of SVRI are quantified in these two SVRMs respectively, and (iv) the Monte Carlo method is adopted to implement the uncertainty of rainfall intensity in SVRMs. Besides, two real-world UDSs of different configurations and scales are used to demonstrate the effectiveness of the developed framework. The application results show that the SVRI could significantly aggravate urban flooding risk including flooding duration and volume, and the impact patterns may vary with the characteristics of UDSs. The results and findings of this study also indicate the importance of taking SVRI into consideration in UDS design and flooding assessment practice. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	14
title_short	Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment
url	https://dx.doi.org/10.1007/s11269-022-03325-8
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author2	Zheng, Feifei Ma, Yiyi Duan, Huan-Feng Chu, Shipeng Deng, Zhengzhi
author2Str	Zheng, Feifei Ma, Yiyi Duan, Huan-Feng Chu, Shipeng Deng, Zhengzhi
ppnlink	315299924
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doi_str	10.1007/s11269-022-03325-8
up_date	2024-07-03T19:17:24.229Z
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Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment

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